This R Markdown file delves into analyzing ransomware infections using data extracted from the Shodan API. By analyzing real-time data on internet-connected devices, we explore ransomware trends across various countries and cities. Through data visualizations and statistical analysis, we aim to identify geographic hotspots of ransomware activity, comprehend infection patterns, and provide valuable insights for cybersecurity professionals. The project underscores the importance of monitoring and comprehending ransomware incidents to enhance global cyber defenses.
dplyr,
ggplot2, tidyr, httr2,
stringr, ggthemes, renv,
plotly, htmltools, maps,
knitr, kableExtra, jsonlite,
lintr, glueThe Shodan API, a powerful tool for searching and retrieving data on internet-connected devices, provides information about devices’ locations, services, vulnerabilities, and more. In this project, the API is used to analyze global trends and patterns of ransomware infections.
This section analyzes ransomware infections. It starts with a summary of affected countries and reported incidents. A statistical analysis presents key metrics on infection distribution. The section concludes with a table detailing ransomware incidents by country and city, revealing geographic trends and high-infection areas.
According to the Shodan dataset, Brazil is the country with the highest number of ransomware infections, with 11 incidents.
There are a total of 103 ransomware infections worldwide!
This comprehensive table offers a detailed breakdown of ransomware infection rates across various countries and cities. It presents country and city names alongside the corresponding number of ransomware incidents, making it easy to compare regions. This table serves as a crucial reference point for understanding global ransomware trends and identifying areas where cyber defenses may need reinforcement.
| Country | City | Number of Infections | |
|---|---|---|---|
| 807 | Germany | Frankfurt am Main | 5 |
| 1719 | Russian Federation | Moscow | 4 |
| 1185 | Turkey | Istanbul | 3 |
| 2027 | Czechia | Prague | 3 |
| 2324 | Mexico | Santiago de Querétaro | 3 |
| 2345 | Brazil | São Paulo | 3 |
| 211 | Spain | Barcelona | 2 |
| 393 | Turkey | Bursa | 2 |
| 699 | Germany | Düsseldorf | 2 |
| 1553 | Brazil | Manaus | 2 |
| 1815 | Germany | Nürnberg | 2 |
| 2276 | Chile | Santiago | 2 |
| 2385 | China | Shanghai | 2 |
| 2421 | China | Shenzhen | 2 |
| 2720 | Mexico | Villahermosa | 2 |
| 16 | Ghana | Accra | 1 |
| 54 | Kazakhstan | Almaty | 1 |
| 77 | Brazil | Aracruz | 1 |
| 113 | Brazil | Araranguá | 1 |
| 162 | Kazakhstan | Astana | 1 |
| 225 | China | Beijing | 1 |
| 257 | Brazil | Boa Esperança | 1 |
| 292 | Belarus | Brest | 1 |
| 325 | Argentina | Buenos Aires | 1 |
| 409 | Egypt | Cairo | 1 |
| 439 | Canada | Calgary | 1 |
| 502 | United States | Cedar Grove | 1 |
| 513 | China | Chongqing | 1 |
| 541 | Argentina | Comodoro Rivadavia | 1 |
| 586 | Colombia | Cúcuta | 1 |
| 646 | United States | Des Moines | 1 |
| 651 | Bangladesh | Dhaka | 1 |
| 735 | Germany | Falkenstein | 1 |
| 765 | China | Foshan | 1 |
| 829 | Argentina | Godoy Cruz | 1 |
| 869 | Brazil | Goiânia | 1 |
| 901 | Argentina | Haedo | 1 |
| 972 | Viet Nam | Hanoi | 1 |
| 986 | Finland | Helsinki | 1 |
| 1042 | United States | Herndon | 1 |
| 1080 | Viet Nam | Ho Chi Minh City | 1 |
| 1097 | India | Hyderābād | 1 |
| 1138 | Pakistan | Islamabad | 1 |
| 1193 | Brazil | Itajaí | 1 |
| 1254 | South Africa | Johannesburg | 1 |
| 1292 | Taiwan | Kaohsiung | 1 |
| 1313 | India | Kolkata | 1 |
| 1353 | Nigeria | Lagos | 1 |
| 1402 | United States | Lee’s Summit | 1 |
| 1428 | Peru | Lima | 1 |
| 1466 | Portugal | Lisbon | 1 |
| 1507 | Spain | Madrid | 1 |
| 1514 | Bahrain | Manama | 1 |
| 1594 | Colombia | Manizales | 1 |
| 1630 | Colombia | Medellín | 1 |
| 1690 | United States | Mercerville | 1 |
| 1755 | Russian Federation | Novyy Urengoy | 1 |
| 1784 | Mexico | Nuevo Laredo | 1 |
| 1856 | Mexico | Ojuelos de Jalisco | 1 |
| 1883 | Czechia | Ostrava | 1 |
| 1920 | Denmark | Otterup | 1 |
| 1967 | Panama | Panama City | 1 |
| 2000 | Mexico | Piedras Negras | 1 |
| 2072 | Mexico | Puebla | 1 |
| 2113 | Poland | Radom | 1 |
| 2158 | United States | Rancho Santa Margarita | 1 |
| 2182 | Pakistan | Rawalpindi | 1 |
| 2201 | Brazil | Rio de Janeiro | 1 |
| 2266 | United States | Santa Fe Springs | 1 |
| 2477 | Singapore | Singapore | 1 |
| 2503 | Macedonia, Republic of | Skopje | 1 |
| 2526 | Bulgaria | Sofia | 1 |
| 2590 | United States | Tacoma | 1 |
| 2627 | Uzbekistan | Tashkent | 1 |
| 2659 | Spain | Tortosa | 1 |
| 2665 | Argentina | Villa Sarmiento | 1 |
| 2767 | Spain | Villanueva de la Cañada | 1 |
| 2801 | Singapore | Woodlands | 1 |
| 2836 | Serbia | Zrenjanin | 1 |
This section visualizes ransomware infection patterns globally. It maps incidents at country and city levels using Shodan API data, highlighting affected regions and trends. An interactive map lets users zoom in and examine infection details, making it useful for cybersecurity professionals and researchers.
This data visualization explores the global distribution of ransomware infections, focusing on the geographical hotspots by country and city. Using data from the Shodan API, the map highlights areas with the highest concentrations of ransomware incidents, shedding light on trends and patterns in cyberattacks. By mapping ransomware infections based on real-time data, the visualization provides insights into which regions are most affected and allows for a better understanding of the geographic spread of these cyber threats. The interactive map enables users to zoom in on specific locations and view detailed information on the number of incidents, cities, and countries impacted, offering valuable insights for cybersecurity professionals and researchers.